HW 03

Initial Setup

if (!require("pacman")) 
  install.packages("pacman")

Loading required package: pacman

pacman::p_load(tidyverse,
               janitor,
               colorspace,
               broom,
               fs,
               scales,
               ggthemes,
               ggrepel,
               patchwork,
               ggimage,
               jpeg,
               glue,
               grid,
               forcats)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

# set width of code output
options(width = 65)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7, # 7" width
  fig.asp = 0.618, # the golden ratio
  fig.retina = 3, # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300 # higher dpi, sharper image
)

1 - Du Bois challenge.

# 1. Base data
du_bois_income <- read_csv("data/income.csv")

Rows: 7 Columns: 7
── Column specification ─────────────────────────────────────────
Delimiter: ","
chr (1): Class
dbl (6): Average_Income, Rent, Food, Clothes, Tax, Other

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# 2. Pivot + cleanup with reversed category order
du_bois_long <- du_bois_income |>
  pivot_longer(cols = Rent:Other, names_to = "Category", values_to = "Expenditure") |>
  mutate(
    Category = fct_relevel(Category, "Rent", "Food", "Clothes", "Tax", "Other"),  # For legend order
    Class = fct_rev(fct_inorder(Class))
  ) |>
  arrange(Class, desc(Category)) |>  # Reverse category order within each class
  group_by(Class) |>
  mutate(
    CenterPos = cumsum(Expenditure) - 0.5 * Expenditure,  # Recalculate positions
    Label = paste0(Expenditure, "%"),
    LabelColor = ifelse(Category == "Rent", "white", "black")
  ) |>
  ungroup()

# 3. Colors (match reference)
colors <- c("Rent" = "black", "Food" = "purple", "Clothes" = "sienna1", "Tax" = "slategray1", "Other" = "snow2")

# 4. Background image
bg_path <- "images/du-bois-bg.jpg"
bg <- jpeg::readJPEG(bg_path)
bg_grob <- rasterGrob(bg, width = unit(1, "npc"), height = unit(1, "npc"), interpolate = TRUE)

# 5. Final Plot
ggplot(du_bois_long, aes(x = Class, y = Expenditure, fill = Category)) +
  annotation_custom(bg_grob, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) +
  geom_col(width = 0.5, color = "white") +
  geom_text(aes(y = CenterPos, label = Label, color = LabelColor),
            size = 3, show.legend = FALSE, family = "mono") +
  scale_color_identity() +
  scale_fill_manual(values = colors, breaks = c("Rent", "Food", "Clothes", "Tax", "Other")) +
  scale_x_discrete(labels = c("1,000     $1,125 \nAND OVER          ", "$750-1000   $880   ", "$500-750   $547   ", "$400-500   $433.82", "$300-400   $335.66", "$200-300   $249.45", "$100-200   $139.10")) +
  coord_flip(clip = "off") +
  labs(
    x = NULL, y = NULL,
    title = "INCOME AND EXPENDITURE OF 150 NEGRO FAMILIES IN ATLANTA, GA., U.S.A."
  ) +
  theme_minimal(base_family = "mono") +
  theme(
    legend.position = "top",
    plot.title = element_text(face = "bold", size = 11, hjust = 0.5),
    panel.grid = element_blank(),
    axis.text.x = element_blank(),
    axis.text.y = element_text(size = 7),
    text = element_text(size = 9),
    legend.title = element_blank(),
    legend.text = element_text(size = 9),
    legend.key.size = unit(0.3, "cm")
  ) +
  annotate(
    geom = "text",
    x = "$100-200",
    y = -0,
    label = "CLASS    ACTUAL AVERAGE                                                                                  \n\n\n",
    size = 2,
    color = "black",
    family = "mono",
    hjust = 0
  )

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